iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discover...

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Autores principales:	Dongxu Zhao, Zhixia Teng, Yanjuan Li, Dong Chen
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	anti-inflammatory peptides random forest feature extraction evolutionary information evolutionary analysis Genetics QH426-470
Acceso en línea:	https://doaj.org/article/df8db1bf7a1247a5841f2c53d7e34254
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spelling	oai:doaj.org-article:df8db1bf7a1247a5841f2c53d7e342542021-12-01T18:34:44ZiAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest1664-802110.3389/fgene.2021.773202https://doaj.org/article/df8db1bf7a1247a5841f2c53d7e342542021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.773202/fullhttps://doaj.org/toc/1664-8021Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.Dongxu ZhaoZhixia TengYanjuan LiDong ChenFrontiers Media S.A.articleanti-inflammatory peptidesrandom forestfeature extractionevolutionary informationevolutionary analysisGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anti-inflammatory peptides random forest feature extraction evolutionary information evolutionary analysis Genetics QH426-470
spellingShingle	anti-inflammatory peptides random forest feature extraction evolutionary information evolutionary analysis Genetics QH426-470 Dongxu Zhao Zhixia Teng Yanjuan Li Dong Chen iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest
description	Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.
format	article
author	Dongxu Zhao Zhixia Teng Yanjuan Li Dong Chen
author_facet	Dongxu Zhao Zhixia Teng Yanjuan Li Dong Chen
author_sort	Dongxu Zhao
title	iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest
title_short	iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest
title_full	iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest
title_fullStr	iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest
title_full_unstemmed	iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest
title_sort	iaips: identifying anti-inflammatory peptides using random forest
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/df8db1bf7a1247a5841f2c53d7e34254
work_keys_str_mv	AT dongxuzhao iaipsidentifyingantiinflammatorypeptidesusingrandomforest AT zhixiateng iaipsidentifyingantiinflammatorypeptidesusingrandomforest AT yanjuanli iaipsidentifyingantiinflammatorypeptidesusingrandomforest AT dongchen iaipsidentifyingantiinflammatorypeptidesusingrandomforest
_version_	1718404725709209600

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

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